High performance multivariate visual data exploration for extremely large data

  • Authors:

  • Oliver Rübel; Prabhat;Kesheng Wu;Hank Childs;Jeremy Meredith;Cameron G. R. Geddes;Estelle Cormier-Michel;Sean Ahern;Gunther H. Weber;Peter Messmer;Hans Hagen;Bernd Hamann;E. Wes Bethel

  • Affiliations:

  • Lawrence Berkeley National Laboratory, Berkeley, CA and University of California, Davis, CA and Technische Universität Kaiserslautern, Kaiserslautern, Germany;Lawrence Berkeley National Laboratory, Berkeley, CA;Lawrence Berkeley National Laboratory, Berkeley, CA;Lawrence Livermore National Laboratory, Livermore, CA;Oak Ridge National Laboratory, Oak Ridge, TN;LOASIS program of Lawrence Berkeley National Laboratory, Berkeley, CA;LOASIS program of Lawrence Berkeley National Laboratory, Berkeley, CA;Oak Ridge National Laboratory, Oak Ridge, TN;Lawrence Berkeley National Laboratory, Berkeley, CA;Tech-X Corporation, Boulder, CO;Technische Universität Kaiserslautern, Kaiserslautern, Germany;Lawrence Berkeley National Laboratory, Berkeley, CA and University of California, Davis, CA and Technische Universität Kaiserslautern, Kaiserslautern, Germany;Lawrence Berkeley National Laboratory, Berkeley, CA and University of California, Davis, CA

  • Venue:
  • Proceedings of the 2008 ACM/IEEE conference on Supercomputing
  • Year:
  • 2008

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Abstract

One of the central challenges in modern science is the need to quickly derive knowledge and understanding from large, complex collections of data. We present a new approach that deals with this challenge by combining and extending techniques from high performance visual data analysis and scientific data management. This approach is demonstrated within the context of gaining insight from complex, time-varying datasets produced by a laser wakefield accelerator simulation. Our approach leverages histogram-based parallel coordinates for both visual information display as well as a vehicle for guiding a data mining operation. Data extraction and subsetting are implemented with state-of-the-art index/query technology. This approach, while applied here to accelerator science, is generally applicable to a broad set of science applications, and is implemented in a production-quality visual data analysis infrastructure. We conduct a detailed performance analysis and demonstrate good scalability on a distributed memory Cray XT4 system.